Fusion of deep learning with adaptive bilateral filter for building outline extraction from remote sensing imagery

Abstract

Solving the problem of building extraction from remote sensing images, which have high spatial resolution, is considered to be one of the most challenging issues in the field of photogrammetry science and remote sensing. The purpose of this study is to present an innovative algorithm, named adaptive bilateral filter (ABF) + segment-based neural network, which is based on the fusion of deep convolutional neural networks (DCNNs) and adaptive ABF and has resulted in improvements in the accuracy of building extraction from remote sensing images with high spatial resolution. The building extraction process in this study includes the following steps: applying the ABF to the research data set and optimizing its parameters in order to improve the building outlines, designing, and training the DCNN, SegNet, based on the improved data set and optimizing it using an adaptive moment estimation algorithm and assessing the impact of applying the ABF + SegNet algorithm to automatic building outline extraction. The proposed algorithm in this study is tested on three sets of remote sensing data from the cities of Potsdam, Indianapolis, and Tehran. The results indicate that the ABF + SegNet algorithm is able to extract the buildings from remote sensing color images with suitable accuracy.